Data from seismic surveys are used in hydrocarbon exploration. A main purpose of imaging in seismic data processing is to place subsurface reflectors in their correct relative spatial relationships to provide an accurate image of a target exploration area. Imaging requires an accurate subsurface acoustic velocity model. A typical velocity model building cycle consists of three steps: migration imaging of seismic data to generate subsurface common image gathers; picking of residual moveouts in the common image gathers; and tomographic inversion for velocity updates using the picking results as input data.
Seismic tomography methods are commonly used to construct the subsurface velocity model of a target exploration area. A typical velocity tomography method requires three inputs: an initial velocity model obtained from well logs and stacking velocity analysis, depth residuals estimated from pre-stack common image gathers, and reflector dip angles estimated from the post-stack image. Reflector dip angles are often used to control the direction of rays along which velocity perturbations are back-projected, and thus can have great impact on the final model to be constructed.
Different from pre-stack common image gathers, which normally contain flat events at near offsets so that picking methods can have a good starting trend to follow, a post-stack image that has been migrated from premature velocity models can be extremely noisy. This is why conventional slant-stack-type reflector dip estimation methods often suffer from conflicting dips and wild swings. A more stable alternative for dip estimation is to manually pick interfaces on the image, followed by computing dip angles along the interfaces being picked and then interpolating between adjacent interfaces to cover the entire image. Obviously, this strategy is not only time-consuming, but also inaccurate for complex interlayer structures.
When seismic traces with different offsets (source-receiver spacing) are migrated and then stacked together, the final output is a post-stack image. This stacked image consists of true reflectors which represent true subsurface geological structures, pseudo reflectors which are coherent migration noise, and random migration noise. To estimate the dip angles of true reflectors, one needs a robust algorithm that does not suffer from strong coherent migration swings and generally low signal-to-noise ratio of the image. The present invention takes advantage of a constrained global inversion technique, which is similar to the residual moveout estimation method described in Sun, “Residual moveout estimation through least squares inversion,” (U.S. Publication No. 2012024166, 2011). This publication is incorporated by reference herein in all jurisdictions that allow it.
To estimate residual moveout, a common image gather is used as the input to the inversion algorithm. Each common image gather has two coordinate axes, vertically the depth, and horizontally the offset. The common image gather is flattened using an inversion algorithm, and the depth shifts between the input gather and the flattened gather are the residuals to be estimated. Similarly, this invention extracts a window of traces from the post stacked image and the window is centered about the reference trace at which we need to estimate the reflector angles for image points along the vertical depth axis. The objective is to flatten all reflectors within the trace window, and the rotation angle between the input reflector and the flattened reflector is the dip angle that we are trying to compute.
To flatten the selected window of traces, we first need to estimate the depth shifts along each reflector. A widely used manual picking technology assumes that each reflector can be approximated by a hyperbolic or parabolic curve. As a result, a selection of the hyperbolic or parabolic curvature is equivalent to the picking of the entire reflector. When the post stack image is contaminated with strong noise, this manual picking method is relatively reliable because users can discriminate between signal and coherent noise using their interpretation knowledge. However, this method is very time consuming which in practice often makes 3-D model building unaffordable. To estimate the depth residuals of all reflectors more efficiently, some automatic picking algorithms have been developed, among which cross-correlation is widely used. These algorithms are computationally efficient, but they tend to generate local solutions to the depth shifts. Furthermore, their solutions are sensitive to the size of the cross-correlation window and are vulnerable to the signal-to-noise ratio of the input image traces.
As noted above, most existing dip angle estimation methods are either time consuming or not reliable. Reliability is critical to the entire iterative model building process because the reflector dips are take-off angles of ray tracing which is used by tomographic inversion and the model updates depend to a considerable degree on how the rays are shot. An objective of the present invention is to efficiently and reliably estimate the reflector dip angles, which ultimately will help to improve the final model building quality.
Existing Technology
U.S. Pat. No. 7,672,824 to Dutta et al. (“Method for shallow water flow detection”) describes an elastic waveform inversion method for building velocity. It is not a dip estimation method.
“Stacking of narrow aperture common shot inversions,” by Bleistein and Cohen (J. Appl. Math. 50, 569-594 (1990)), describes a common shot inversion method for better image stacking. It also is not a dip estimation method.
U.S. Pat. No. 6,850,864 to Gillard et al. (“Method for analyzing dip in seismic data volumes”) describes a method that directly computes the arctangent of the ratio of the horizontal and vertical gradient, followed by comparing with other possible dips. This is a dip scan method which is highly efficient and is very accurate for clean data. However, it is sensitive to the window size and the data S/N ratio, and leads to a local solution to the dip estimation problem.
Lee (2001) describes how to avoid pitfalls in seismic data flattening. This method attempts to determine the bed thickness and reconstruct the paleosurfaces, both of which are important parts of seismic interpretation. For instance, meandering channels are better focused on a horizontal slice with data flattening being taken into account. To flatten the data, however, horizons need to be picked throughout the entire data volume which turns out to be very time consuming.
Lomask et al. (2006) present an efficient dense-picking method for flattening seismic data. This method first estimates local dips of pre-stack seismic events, then converts the local dips into time shifts. Their paper contains applications to pre-stack data volumes, not post-stack image or image gathers to which model building is closely related. The quality of their flattening results can be heavily affected by noise in the data where coherent crossing events cannot be effectively handled. In addition, when applied to reflector dip estimation, this method may not converge to the global minima.
Siliqi et al. (2007) introduces an automatic picking technique for dense estimation of high order residual moveout curves. This method uses Taylor series expansions to approximate the real curvature of the event and requires that the curvature is smooth enough so that lower terms of the Taylor series can be successfully fit. As a result, this method is less effective for post stack image with high frequency noise and complex curvatures.
Kurin and Glogovsky (2003) developed a technique to pick seismic events for normal moveout analysis. Their paper presents a practical approach to the travel-time picking which accounts for the fourth-order term in the common mid-point normal moveout formula. The addition of the fourth-order term to the conventional hyperbolic equation helps to improve the picking results in case non-hyperbolic events exist in the data. However, the approach relies on a successful fitting of the hyperbola and is not practical for post stack image with complicated structures. In addition, their algorithm is only semi-automated and needs a longer turn-around time to allow for user input to guide the algorithm.
There is therefore a need for a technique that is computationally efficient, has no hyperbolic or parabolic assumption, and at the same time is robust and reliable for computing high-resolution complex reflector dip angles in the post stack migration image.